How to plot all labels similar to the picture, with each label in its own box in a confusion matrix?

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machine-learning

I don't understand why my code produces a graph that looks complicated and difficult to understand. How do I adjust my code so that it plots something similar to the picture but instead of just '3's and '5's, I want to plot all the numbers in the confusion matrix.

My overall objective is classifying digits from 0 to 9 so I want to display all numbers 0-9 in a confusion matrix to show whether the decision tree algorithm was successful.

Picture of what I want to achieve:https://i.stack.imgur.com/hgY2R.png[^]

What I have tried:

# Define all possible classes (numbers 0-9)
classes = np.arange(10)

# Initialize a figure to plot the confusion matrix
size = 5
pad = 0.2
fig6,ax6 = plt.subplots(figsize=(size, size), layout='constrained')

# Loop over all pairs of true and predicted labels
for true_label in classes:
    for pred_label in classes:
        # Extract examples where true label is true_label and predicted label is pred_label
        examples = X_test[(y_test == str(true_label)) & (y_pred_dt == str(pred_label))]
        
        # Plot the examples
        for idx, image_data in enumerate(examples[:size*size]):
            x = idx % size + pred_label * (size + pad)
            y = idx // size + true_label * (size + pad)
            ax6.imshow(image_data.reshape(28, 28), cmap="binary",
                       extent=(x, x + 1, y, y + 1))

# Set ticks and labels
ax6.set_xticks([size / 2 + i * (size + pad) for i in range(len(classes))], labels=classes)
ax6.set_yticks([size / 2 + i * (size + pad) for i in range(len(classes))], labels=classes)

# Show grid lines
ax6.plot([size + pad / 2, size + pad / 2], [0, len(classes) * (size + pad)], "k:")
ax6.plot([0, len(classes) * (size + pad)], [size + pad / 2, size + pad / 2], "k:")

# Set axis limits
ax6.axis([0, len(classes) * (size + pad), 0, len(classes) * (size + pad)])

# Set axis labels
ax6.set_xlabel("Predicted label")
ax6.set_ylabel("True label")

# Show the plot
plt.show()

Picture of output: https://i.stack.imgur.com/dKzgJ.png[^]